{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T22:17:19.603440Z",
     "start_time": "2019-02-12T22:17:19.523397Z"
    }
   },
   "source": [
    "<h1><center> Facial Emotion Recognition - HOG, Landmarks and sliding windows </center></h1>\n",
    "<center> A project for the French Employment Agency </center>\n",
    "<center> Telecom ParisTech 2018-2019 </center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I. Context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim of this notebook is to explore facial emotion recognition techniques from a live webcam video stream. \n",
    "\n",
    "The data set used for training is the Kaggle FER2013 emotion recognition data set : https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data\n",
    "\n",
    "The models explored include :\n",
    "- Manual filters \n",
    "- Deep Learning Architectures\n",
    "- DenseNet Inspired Architectures\n",
    "\n",
    "This model will be combined with voice emotion recongition as well as psychological traits extracted from text inputs, and should provide a benchmark and a deep analysis of both verbal and non-verbal insights for candidates seeking for a job and their performance during an interview."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# II. General imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Versions used :"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Python : 3.6.5\n",
    "Tensorflow : 1.10.1\n",
    "Keras : 2.2.2\n",
    "Numpy : 1.15.4\n",
    "OpenCV : 4.0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T17:07:04.801492Z",
     "start_time": "2019-03-07T17:06:59.260412Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n",
      "/anaconda3/lib/python3.6/site-packages/requests/__init__.py:80: RequestsDependencyWarning: urllib3 (1.21.1) or chardet (2.3.0) doesn't match a supported version!\n",
      "  RequestsDependencyWarning)\n"
     ]
    }
   ],
   "source": [
    "### General imports ###\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from time import time\n",
    "from time import sleep\n",
    "import re\n",
    "import os\n",
    "import argparse\n",
    "from collections import OrderedDict\n",
    "import matplotlib.animation as animation\n",
    "\n",
    "### Image processing ###\n",
    "from scipy.ndimage import zoom\n",
    "from scipy.spatial import distance\n",
    "import imutils\n",
    "from scipy import ndimage\n",
    "import cv2\n",
    "import dlib\n",
    "from __future__ import division\n",
    "from imutils import face_utils\n",
    "\n",
    "### HOG and Landmarks models ###\n",
    "import scipy.misc\n",
    "import dlib\n",
    "import cv2\n",
    "from skimage.feature import hog\n",
    "\n",
    "### Build SVM models ###\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import svm\n",
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "### Same trained models ###\n",
    "import h5py\n",
    "from keras.models import model_from_json\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# III. Import datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T17:07:04.813548Z",
     "start_time": "2019-03-07T17:07:04.806244Z"
    }
   },
   "outputs": [],
   "source": [
    "path = '/Users/maelfabien/filrouge_pole_emploi/Video/'\n",
    "local_path = '/Users/maelfabien/Desktop/LocalDB/Videos/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T17:07:40.179459Z",
     "start_time": "2019-03-07T17:07:04.816603Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pd.options.mode.chained_assignment = None  # default='warn'  #to suppress SettingWithCopyWarning\n",
    "\n",
    "#Reading the dataset\n",
    "dataset = pd.read_csv(local_path + 'fer2013.csv')\n",
    "\n",
    "#Obtaining train data where usage is \"Training\"\n",
    "train = dataset[dataset[\"Usage\"] == \"Training\"]\n",
    "\n",
    "#Obtaining test data where usage is \"PublicTest\"\n",
    "test = dataset[dataset[\"Usage\"] == \"PublicTest\"]\n",
    "\n",
    "#Converting \" \" separated pixel values to list\n",
    "train['pixels'] = train['pixels'].apply(lambda image_px : np.fromstring(image_px, sep = ' '))\n",
    "test['pixels'] = test['pixels'].apply(lambda image_px : np.fromstring(image_px, sep = ' '))\n",
    "dataset['pixels'] = dataset['pixels'].apply(lambda image_px : np.fromstring(image_px, sep = ' '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T09:59:42.120935Z",
     "start_time": "2019-03-07T09:59:42.096419Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emotion</th>\n",
       "      <th>pixels</th>\n",
       "      <th>Usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>151 150 147 155 148 133 111 140 170 174 182 15...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>231 212 156 164 174 138 161 173 182 200 106 38...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emotion                                             pixels     Usage\n",
       "0        0  70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...  Training\n",
       "1        0  151 150 147 155 148 133 111 140 170 174 182 15...  Training\n",
       "2        2  231 212 156 164 174 138 161 173 182 200 106 38...  Training\n",
       "3        4  24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...  Training\n",
       "4        6  4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...  Training"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T09:59:42.915620Z",
     "start_time": "2019-03-07T09:59:42.148896Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.hist(dataset['emotion'], bins=30)\n",
    "plt.title(\"Distribution of the number of images per emotion\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T09:59:42.926343Z",
     "start_time": "2019-03-07T09:59:42.919087Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28709, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T09:59:42.938492Z",
     "start_time": "2019-03-07T09:59:42.930996Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3589, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IV. Extract image features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now extract the following features :\n",
    "- HOG sliding windows\n",
    "- HOG features\n",
    "- Facial landmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T17:07:51.914382Z",
     "start_time": "2019-03-07T17:07:51.908141Z"
    }
   },
   "outputs": [],
   "source": [
    "pictures = dataset['pixels']\n",
    "labels = dataset['emotion']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T17:07:52.613725Z",
     "start_time": "2019-03-07T17:07:52.603949Z"
    }
   },
   "outputs": [],
   "source": [
    "shape_x = 48\n",
    "shape_y = 48\n",
    "window_size = 24\n",
    "window_step = 6\n",
    "\n",
    "ONE_HOT_ENCODING = False\n",
    "SAVE_IMAGES = False\n",
    "GET_LANDMARKS = False\n",
    "GET_HOG_FEATURES = False\n",
    "GET_HOG_WINDOWS_FEATURES = False\n",
    "SELECTED_LABELS = []\n",
    "IMAGES_PER_LABEL = 500\n",
    "\n",
    "OUTPUT_FOLDER_NAME = \"/Users/maelfabien/Desktop/LocalDB/Videos/Face_Features/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T17:07:54.564659Z",
     "start_time": "2019-03-07T17:07:53.546172Z"
    }
   },
   "outputs": [],
   "source": [
    "predictor = dlib.shape_predictor('/Users/maelfabien/Desktop/LocalDB/Videos/landmarks/shape_predictor_68_face_landmarks.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T17:08:40.285845Z",
     "start_time": "2019-03-07T17:08:40.281529Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_landmarks(image, rects):\n",
    "    if len(rects) > 1:\n",
    "        raise BaseException(\"TooManyFaces\")\n",
    "    if len(rects) == 0:\n",
    "        raise BaseException(\"NoFaces\")\n",
    "    return np.matrix([[p.x, p.y] for p in predictor(image, rects[0]).parts()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T17:08:52.379631Z",
     "start_time": "2019-03-07T17:08:52.372813Z"
    }
   },
   "outputs": [],
   "source": [
    "def sliding_hog_windows(image):\n",
    "    hog_windows = []\n",
    "    for y in range(0, shape_x, window_step):\n",
    "        for x in range(0, shape_y, window_step):\n",
    "            window = image[y:y+window_size, x:x+window_size]\n",
    "            hog_windows.extend(hog(window, orientations=8, pixels_per_cell=(8, 8),\n",
    "                                            cells_per_block=(1, 1), visualise=False))\n",
    "    return hog_windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T17:23:08.034321Z",
     "start_time": "2019-03-07T17:08:53.074595Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/skimage/feature/_hog.py:150: skimage_deprecation: Default value of `block_norm`==`L1` is deprecated and will be changed to `L2-Hys` in v0.15. To supress this message specify explicitly the normalization method.\n",
      "  skimage_deprecation)\n",
      "/anaconda3/lib/python3.6/site-packages/skimage/feature/_hog.py:248: skimage_deprecation: Argument `visualise` is deprecated and will be changed to `visualize` in v0.16\n",
      "  'be changed to `visualize` in v0.16', skimage_deprecation)\n",
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:30: DeprecationWarning: `imsave` is deprecated!\n",
      "`imsave` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``imageio.imwrite`` instead.\n"
     ]
    }
   ],
   "source": [
    "images = []\n",
    "labels_list = []\n",
    "landmarks = []\n",
    "\n",
    "hog_slide_features = []\n",
    "hog_slide_images = []\n",
    "\n",
    "hog_features = []\n",
    "hog_images = []\n",
    "\n",
    "for i in range(len(pictures)):\n",
    "    try:\n",
    "        # Build the image as an array\n",
    "        image = pictures[i].reshape((shape_x, shape_y))\n",
    "        images.append(pictures[i])\n",
    "        \n",
    "        # HOG sliding windows features\n",
    "        features = sliding_hog_windows(image)\n",
    "        f, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16),cells_per_block=(1, 1), visualise=True)\n",
    "        hog_slide_features.append(features)\n",
    "        hog_slide_images.append(hog_image)\n",
    "        \n",
    "        # HOG features\n",
    "        features, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16),\n",
    "                                            cells_per_block=(1, 1), visualise=True)\n",
    "        hog_features.append(features)\n",
    "        hog_images.append(hog_image)\n",
    "\n",
    "        # Facial landmarks\n",
    "        scipy.misc.imsave('temp.jpg', image)\n",
    "        image2 = cv2.imread('temp.jpg')\n",
    "        face_rects = [dlib.rectangle(left=1, top=1, right=47, bottom=47)]\n",
    "        face_landmarks = get_landmarks(image2, face_rects)\n",
    "        landmarks.append(face_landmarks)            \n",
    "        \n",
    "        # Labels\n",
    "        labels_list.append(labels[i])\n",
    "        #nb_images_per_label[labels[i]] += 1\n",
    "        \n",
    "    except Exception as e:\n",
    "        print( \"error in image: \" + str(i) + \" - \" + str(e))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the arrays :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T17:24:17.122431Z",
     "start_time": "2019-03-07T17:23:53.303973Z"
    }
   },
   "outputs": [],
   "source": [
    "np.save(OUTPUT_FOLDER_NAME + 'labels.npy', labels_list)\n",
    "\n",
    "np.save(OUTPUT_FOLDER_NAME + 'hog_slide_image.npy', hog_slide_images)\n",
    "np.save(OUTPUT_FOLDER_NAME + 'hog_slide_features.npy', hog_slide_features)\n",
    "\n",
    "np.save(OUTPUT_FOLDER_NAME + 'hog_image.npy', hog_images)\n",
    "np.save(OUTPUT_FOLDER_NAME + 'hog_features.npy', hog_features)\n",
    "\n",
    "np.save(OUTPUT_FOLDER_NAME + 'landmarks.npy', landmarks)\n",
    "np.save(OUTPUT_FOLDER_NAME + 'images.npy', images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Re-open them :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T11:43:36.955974Z",
     "start_time": "2019-03-07T11:43:34.598279Z"
    }
   },
   "outputs": [],
   "source": [
    "labels_list = np.load(OUTPUT_FOLDER_NAME + 'labels.npy')\n",
    "\n",
    "hog_slide_images = np.load(OUTPUT_FOLDER_NAME + 'hog_slide_image.npy')\n",
    "hog_slide_features = np.load(OUTPUT_FOLDER_NAME + 'hog_slide_features.npy')\n",
    "\n",
    "hog_images = np.load(OUTPUT_FOLDER_NAME + 'hog_image.npy')\n",
    "hog_features = np.load(OUTPUT_FOLDER_NAME + 'hog_features.npy')\n",
    "\n",
    "landmarks = np.load(OUTPUT_FOLDER_NAME + 'landmarks.npy')\n",
    "images = np.load(OUTPUT_FOLDER_NAME + 'images.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# V. Visualize HOG Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T10:57:31.537239Z",
     "start_time": "2019-03-07T10:57:28.000560Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    fig, axs = plt.subplots(nrows=1, ncols=2, sharex=True)\n",
    "    ax = axs[0]\n",
    "    ax.imshow(images[i].reshape((shape_x, shape_y)))\n",
    "    ax.set_title('Face')\n",
    "\n",
    "    ax = axs[1]\n",
    "    ax.imshow(hog_images[i])\n",
    "    ax.set_title('HOG image')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VI. Build the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the train and test sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge the different data sets :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T11:43:41.018066Z",
     "start_time": "2019-03-07T11:43:40.839497Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28709, 136)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landmarks = np.array([x.flatten() for x in landmarks])\n",
    "landmarks.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T11:43:43.421263Z",
     "start_time": "2019-03-07T11:43:42.684683Z"
    }
   },
   "outputs": [],
   "source": [
    "data_0 = hog_features\n",
    "data_1 = landmarks\n",
    "data_2 = np.concatenate((landmarks, hog_features), axis=1)\n",
    "data_3 = np.concatenate((landmarks, hog_slide_features), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T11:43:43.435791Z",
     "start_time": "2019-03-07T11:43:43.426125Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((28709, 72), (28709, 136), (28709, 208), (28709, 2728))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_0.shape, data_1.shape, data_2.shape, data_3.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hog Features only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T11:43:46.752249Z",
     "start_time": "2019-03-07T11:43:46.732279Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data_0, labels_list, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T11:47:54.091463Z",
     "start_time": "2019-03-07T11:45:23.151108Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time :  122.60713601112366\n",
      "Accuracy :  0.32836444692114797\n"
     ]
    }
   ],
   "source": [
    "model = OneVsRestClassifier(SVC(random_state=42, max_iter=10000, kernel='rbf',gamma='auto'))\n",
    "\n",
    "# Train\n",
    "start_time = time()\n",
    "model.fit(X_train, y_train)\n",
    "training_time = time() - start_time\n",
    "print(\"Training time : \", training_time)\n",
    "\n",
    "# Predict\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy_hog = accuracy_score(y_pred, y_test)\n",
    "print(\"Accuracy : \", accuracy_hog)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Landmarks features only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T11:49:58.555002Z",
     "start_time": "2019-03-07T11:49:58.511747Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data_1, labels_list, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:38:20.266109Z",
     "start_time": "2019-03-07T11:50:00.103557Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time :  2793.0992209911346\n",
      "Accuracy :  0.4639175257731959\n"
     ]
    }
   ],
   "source": [
    "model = OneVsRestClassifier(SVC(random_state=42, max_iter=10000, kernel='rbf',gamma='auto'))\n",
    "\n",
    "# Train\n",
    "start_time = time()\n",
    "model.fit(X_train, y_train)\n",
    "training_time = time() - start_time\n",
    "print(\"Training time : \", training_time)\n",
    "\n",
    "# Predict\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy_hog = accuracy_score(y_pred, y_test)\n",
    "print(\"Accuracy : \", accuracy_hog)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HOG & landmarks features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T13:26:12.503352Z",
     "start_time": "2019-03-07T13:26:12.289716Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data_2, labels_list, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T13:42:33.952183Z",
     "start_time": "2019-03-07T13:26:17.508171Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time :  801.6945312023163\n",
      "Accuracy :  0.4752020061298412\n"
     ]
    }
   ],
   "source": [
    "model = OneVsRestClassifier(SVC(random_state=42, max_iter=10000, kernel='rbf',gamma='auto'))\n",
    "\n",
    "# Train\n",
    "start_time = time()\n",
    "model.fit(X_train, y_train)\n",
    "training_time = time() - start_time\n",
    "print(\"Training time : \", training_time)\n",
    "\n",
    "# Predict\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy_hog = accuracy_score(y_pred, y_test)\n",
    "print(\"Accuracy : \", accuracy_hog)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sliding window HOG & landmarks features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T13:44:03.774095Z",
     "start_time": "2019-03-07T13:44:00.805305Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data_3, labels_list, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T13:47:07.172824Z",
     "start_time": "2019-03-07T13:44:14.507122Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:244: ConvergenceWarning: Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time :  135.3525631427765\n",
      "Accuracy :  0.24686542212315407\n"
     ]
    }
   ],
   "source": [
    "model = OneVsRestClassifier(SVC(random_state=42, max_iter=100, kernel='rbf',gamma='auto'))\n",
    "\n",
    "# Train\n",
    "start_time = time()\n",
    "model.fit(X_train, y_train)\n",
    "training_time = time() - start_time\n",
    "print(\"Training time : \", training_time)\n",
    "\n",
    "# Predict\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy_hog = accuracy_score(y_pred, y_test)\n",
    "print(\"Accuracy : \", accuracy_hog)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VII. Sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Visualization : https://github.com/JostineHo/mememoji/blob/master/data_visualization.ipynb\n",
    "- State of the art Architecture : https://github.com/amineHorseman/facial-expression-recognition-using-cnn\n",
    "- Eyes Tracking : https://www.pyimagesearch.com/2017/04/24/eye-blink-detection-opencv-python-dlib/\n",
    "- Face Alignment : https://www.pyimagesearch.com/2017/05/22/face-alignment-with-opencv-and-python/\n",
    "- C.Pramerdorfer,  and  M.Kampel.Facial  Expression  Recognition  using  Con-volutional  Neural  Networks:  State  of  the  Art.  Computer  Vision  Lab,  TU  Wien. https://arxiv.org/pdf/1612.02903.pdf\n",
    "- A Brief Review of Facial Emotion Recognition Based\n",
    "on Visual Information : https://www.mdpi.com/1424-8220/18/2/401/pdf\n",
    "- Going deeper in facial expression recognition using deep neural networks : https://ieeexplore.ieee.org/document/7477450\n",
    "- Emotional Deep Alignment Network paper : https://arxiv.org/abs/1810.10529\n",
    "- Emotional Deep Alignment Network github : https://github.com/IvonaTau/emotionaldan\n",
    "- HOG, Landmarks and SVM : https://github.com/amineHorseman/facial-expression-recognition-svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
